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Die vorliegende empirische Untersuchung befasst sich mit einer Umfrage zur Wörterbuchbenutzung bei 41 Studentinnen und Studenten des Dipartimento di Filologia, Letteratura e Linguistica der Universität Pisa, dasselbe Department, an dem auch das deutsch-italienische sprachwissenschaftliche Online-Wörterbuch DIL erarbeitet worden ist (vgl. Flinz: 2011). Die schriftliche Umfrage wurde in Anlehnung an Hartmanns 5. Hypothese „An analysis of users´ needs should precede dictionary design“ (1989) durchgeführt. Die wichtigsten Ergebnisse waren von großer Bedeutung für die Gestaltung der makro- und mikrostrukturellen Eigenschaften des Fachwörterbuches. Die Ergebnisse der Untersuchung und die daraus folgenden Reflektionen werden in thematischen Kernblöcken vorgestellt.
We present an experimental approach to determining natural dimensions of story comparison. The results show that untrained test subjects generally do not privilege structural information. When asked to justify sameness ratings, they may refer to content, but when asked to state differences, they mostly refer to style, concrete events, details and motifs. We conclude that adequate formal models of narratives must represent such non-structural data.
The newest generation of speech technology caused a huge increase of audio-visual data nowadays being enhanced with orthographic transcripts such as in automatic subtitling in online platforms. Research data centers and archives contain a range of new and historical data, which are currently only partially transcribed and therefore only partially accessible for systematic querying. Automatic Speech Recognition (ASR) is one option of making that data accessible. This paper tests the usability of a state-of-the-art ASR-System on a historical (from the 1960s), but regionally balanced corpus of spoken German, and a relatively new corpus (from 2012) recorded in a narrow area. We observed a regional bias of the ASR-System with higher recognition scores for the north of Germany vs. lower scores for the south. A detailed analysis of the narrow region data revealed – despite relatively high ASR-confidence – some specific word errors due to a lack of regional adaptation. These findings need to be considered in decisions on further data processing and the curation of corpora, e.g. correcting transcripts or transcribing from scratch. Such geography-dependent analyses can also have the potential for ASR-development to make targeted data selection for training/adaptation and to increase the sensitivity towards varieties of pluricentric languages.
Uncertain about Uncertainty: Different ways of processing fuzziness in digital humanities data
(2014)
The GeoBib project is constructing a georeferenced online bibliography of early Holocaust and camp literature published between 1933 and 1949 (Entrup et al. 2013a). Our immediate objectives include identifying the texts of interest in the first place, composing abstracts for them, researching their history, and annotating relevant places and times. Relations between persons, texts, and places will be visualized using digital maps and GIS software as an integral part of the resulting GeoBib information portal. The combination of diverse data from varying sources not only enriches our knowledge of these otherwise mostly forgotten texts; it also confronts us with vague, uncertain or even conflicting information. This situation yields challenges for all researchers involved – historians, literary scholars, geographers and computer scientists alike. While the project operates at the intersection of historical and literary studies, the involved computer scientists are in charge of providing a working environment (Entrup et al. 2013b) and processing the collected information in a way that is formalized yet capable of dealing with inevitable vagueness, uncertainty and contradictions. In this paper we focus on the problems and opportunities of encoding and processing fuzzy data.
The paper presents a discussion on the main linguistic phenomena of user-generated texts found in web and social media, and proposes a set of annotation guidelines for their treatment within the Universal Dependencies (UD) framework. Given on the one hand the increasing number of treebanks featuring user-generated content, and its somewhat inconsistent treatment in these resources on the other, the aim of this paper is twofold: (1) to provide a short, though comprehensive, overview of such treebanks - based on available literature - along with their main features and a comparative analysis of their annotation criteria, and (2) to propose a set of tentative UD-based annotation guidelines, to promote consistent treatment of the particular phenomena found in these types of texts. The main goal of this paper is to provide a common framework for those teams interested in developing similar resources in UD, thus enabling cross-linguistic consistency, which is a principle that has always been in the spirit of UD.
We explore the feasibility of contextual healthiness classification of food items. We present a detailed analysis of the linguistic phenomena that need to be taken into consideration for this task based on a specially annotated corpus extracted from web forum entries. For automatic classification, we compare a supervised classifier and rule-based classification. Beyond linguistically motivated features that include sentiment information we also consider the prior healthiness of food items.